EDA

Import Packages

# Import Packages
pacman::p_load(sf, tmap, tidyverse, sfdep, readxl, Kendall, plotly, plyr, SpatialAcc, ggstatsplot, reshape2, fca, sp, caret, stplanr, broom, geojsonio, jsonlite, dplyr)

Import Data

MPSZ

# Import MPSZ
mpsz <- st_read(dsn = "data/geospatial", layer = "MPSZ-2019")

Reading layer `MPSZ-2019' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84

mpsz <- st_transform(mpsz, 3414)

is.null(mpsz)

[1] FALSE

mpsz <- st_make_valid(mpsz)

write_rds(mpsz, "data/model/mpsz.rds")

MRT

# Import MRT Stations
mrt <- st_read(dsn = "data/geospatial", layer = "RapidTransitSystemStation")

Reading layer `RapidTransitSystemStation' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 220 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 6068.209 ymin: 27478.44 xmax: 45377.5 ymax: 47913.58
Projected CRS: SVY21

mrt_sf <- st_transform(mrt, 3414)

mrt_sf <- mrt_sf %>%
  filter(str_detect(mrt_sf$STN_NAM_DE, "MRT STATION|LRT STATION")) %>%
  dplyr::select(c(4, 3, 5))

mrt_sf[rowSums(is.na(mrt_sf))!=0,]

Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] STN_NAM_DE TYP_CD_DES geometry  
<0 rows> (or 0-length row.names)

st_is_valid(mrt_sf)

  [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [49]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [61]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [73]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [85]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [97]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[109]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[121]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[133]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[145]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[157]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[169]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[181]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[193]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
[205]  TRUE

mrt_sf <- st_make_valid(mrt_sf)

write_rds(mrt_sf, "data/model/mrt_sf.rds")

MRT Rail Line

# MRT Rail Line
rail <- st_read(dsn = "data/geospatial", layer = "G_MP14_RAIL_LI")

Reading layer `G_MP14_RAIL_LI' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 1399 features and 9 fields (with 2 geometries empty)
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: 5950.856 ymin: 26030.91 xmax: 45473.16 ymax: 48252.23
Projected CRS: SVY21

rail_sf <- st_transform(rail, 3414)

rail_sf <- rail_sf %>%
  filter(TYPE %in%
           c("MRT",
             "LRT",
             "CCL")) %>%
  dplyr::select(c(1))

rail_sf[rowSums(is.na(rail_sf))!=0,]

Simple feature collection with 0 features and 1 field
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] OBJECTID geometry
<0 rows> (or 0-length row.names)

st_is_valid(rail_sf)

   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [323] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [407] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [435] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [463] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [491] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [519] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [575] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [589] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [603] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [645] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [659] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [673] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [687] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [715] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [743] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [771] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [799] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [827] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [855] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [897] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [911] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [939] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [967] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [995] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1023] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1037] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1051] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1065] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1079] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1093] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1107] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1135] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1163] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1177] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1191] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1219] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1247] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1275] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1303] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1317] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1345] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1359] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1387] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

rail_sf <- st_make_valid(rail_sf)

is.null(rail_sf)

[1] FALSE

rail_sf <- na.omit(rail_sf)

summary(rail_sf)

    OBJECTID                 geometry   
 Min.   :   1.0   MULTILINESTRING:1396  
 1st Qu.: 351.8   epsg:3414      :   0  
 Median : 700.5   +proj=tmer...  :   0  
 Mean   : 700.5                         
 3rd Qu.:1049.2                         
 Max.   :1399.0                         

write_rds(rail_sf, "data/model/rail_sf.rds")

Tourism

# Tourism
tourism_sf <- st_read(dsn = "data/geospatial", layer = "TOURISM")

Reading layer `TOURISM' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 107 features and 19 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -1.797693e+308 ymin: -1.797693e+308 xmax: 43659.54 ymax: 47596.73
Projected CRS: SVY21

# Assign EPSG Code
tourism_sf <- st_transform(tourism_sf, 3414) %>%
  dplyr::select(c(6, 10, 20))

tourism_sf[rowSums(is.na(tourism_sf))!=0,]

Simple feature collection with 2 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -1.797693e+308 ymin: -1.797693e+308 xmax: 28447.06 ymax: 28853.86
Projected CRS: SVY21 / Singapore TM
                PAGETITLE ADDRESS                       geometry
4              Shophouses    <NA>      POINT (28447.06 28853.86)
73 Cruises from Singapore    <NA> POINT (-1.797693e+308 -1.79...

tourism_sf <- na.omit(tourism_sf,c("Shophouses", "Cruises from Singapore"))

is(tourism_sf, "sf")

[1] TRUE

write_rds(tourism_sf, "data/model/tourism_sf.rds")

Shopping Malls

# Shopping Malls
shopping <- read.csv("data/geospatial/mall_coordinates.csv")

shopping <- shopping %>%
  dplyr::select(name, latitude, longitude)

shopping_sf <- st_as_sf(shopping,
                              coords = c("longitude",
                                         "latitude"),
                              crs = 4326) %>%
  st_transform(crs = 3414)

write_rds(shopping_sf, "data/model/shopping_sf.rds")

Childcare

# Childcare
childcare_sf <- st_read(dsn = "data/geospatial", layer = "CHILDCARE")

Reading layer `CHILDCARE' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 1545 features and 15 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 11203.01 ymin: 25667.6 xmax: 45404.24 ymax: 49300.88
Projected CRS: WGS_1984_Transverse_Mercator

# Assign EPSG Code
childcare_sf <- st_transform(childcare_sf, 3414) %>%
  dplyr::select(c(10, 4))

childcare_sf[rowSums(is.na(childcare_sf))!=0,]

Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME       ADDRESSSTR geometry  
<0 rows> (or 0-length row.names)

st_is_valid(childcare_sf)

   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [323] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [407] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [435] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [463] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [491] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [519] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [575] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [589] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [603] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [645] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [659] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [673] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [687] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [715] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [743] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [771] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [799] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [827] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [855] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [897] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [911] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [939] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [967] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [995] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1023] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1037] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1051] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1065] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1079] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1093] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1107] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1135] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1163] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1177] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1191] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1219] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1247] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1275] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1303] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1317] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1345] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1359] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1387] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1401] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1415] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1429] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1443] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1471] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1485] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1499] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1513] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1527] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1541] TRUE TRUE TRUE TRUE TRUE

rail_sf <- st_make_valid(childcare_sf)

is.null(childcare_sf)

[1] FALSE

childcare_sf <- na.omit(childcare_sf)

write_rds(childcare_sf, "data/new/childcare_sf.rds")

Eldercare

# Eldercare
eldercare_sf <- st_read(dsn = "data/geospatial", layer = "ELDERCARE")

Reading layer `ELDERCARE' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 133 features and 18 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 14481.92 ymin: 28218.43 xmax: 41665.14 ymax: 46804.9
Projected CRS: SVY21

# Assign EPSG Code
eldercare_sf <- st_transform(eldercare_sf, 3414) %>%
  dplyr::select(c(11, 5, 4))

eldercare_sf[rowSums(is.na(eldercare_sf))!=0,]

Simple feature collection with 0 features and 3 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME       ADDRESSSTR ADDRESSPOS geometry  
<0 rows> (or 0-length row.names)

rail_sf <- st_make_valid(eldercare_sf)

is.null(eldercare_sf)

[1] FALSE

eldercare_sf <- na.omit(eldercare_sf)

write_rds(eldercare_sf, "data/new/eldercare_sf.rds")

Hawker Centre

# Hawker Centre
hawker_sf <- st_read(dsn = "data/geospatial", layer = "HAWKERCENTRE")

Reading layer `HAWKERCENTRE' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 125 features and 21 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 12874.19 ymin: 28355.97 xmax: 45241.4 ymax: 47872.53
Projected CRS: SVY21

# Assign EPSG Code
hawker_sf <- st_transform(hawker_sf, 3414) %>%
  dplyr::select(c(13))

hawker_sf[rowSums(is.na(hawker_sf))!=0,]

Simple feature collection with 0 features and 1 field
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME     geometry
<0 rows> (or 0-length row.names)

rail_sf <- st_make_valid(hawker_sf)

is.null(hawker_sf)

[1] FALSE

hawker_sf <- na.omit(hawker_sf)

write_rds(hawker_sf, "data/new/hawker_sf.rds")

Kindergartens

# Kindergartens
kindergarten_sf <- st_read(dsn = "data/geospatial", layer = "KINDERGARTENS")

Reading layer `KINDERGARTENS' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 448 features and 15 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 11909.7 ymin: 25596.33 xmax: 43395.47 ymax: 48562.06
Projected CRS: SVY21

# Assign EPSG Code
kindergarten_sf <- st_transform(kindergarten_sf, 3414) %>%
  dplyr::select(c(11, 5))

kindergarten_sf[rowSums(is.na(kindergarten_sf))!=0,]

Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME       ADDRESSSTR geometry  
<0 rows> (or 0-length row.names)

rail_sf <- st_make_valid(kindergarten_sf)

is.null(kindergarten_sf)

[1] FALSE

kindergarten_sf <- na.omit(kindergarten_sf)

write_rds(kindergarten_sf, "data/new/kindergarten_sf.rds")

National Park

# National Park
national_sf <- st_read(dsn = "data/geospatial", layer = "NATIONALPARKS")

Reading layer `NATIONALPARKS' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 352 features and 15 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 12373.11 ymin: 21869.93 xmax: 46735.95 ymax: 49231.09
Projected CRS: SVY21

# Assign EPSG Code
national_sf <- st_transform(national_sf, 3414) %>%
  dplyr::select(c(7))

national_sf[rowSums(is.na(national_sf))!=0,]

Simple feature collection with 0 features and 1 field
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME     geometry
<0 rows> (or 0-length row.names)

national_sf <- st_make_valid(national_sf)

is.null(national_sf)

[1] FALSE

national_sf <- na.omit(national_sf)

write_rds(national_sf, "data/new/national_sf.rds")

Supermarkets

# Supermarkets
supermarkets_sf <- st_read(dsn = "data/geospatial", layer = "SUPERMARKETS")

Reading layer `SUPERMARKETS' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 526 features and 8 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4901.188 ymin: 25529.08 xmax: 46948.22 ymax: 49233.6
Projected CRS: SVY21

# Assign EPSG Code
supermarkets_sf <- st_transform(supermarkets_sf, 3414) %>%
  dplyr::select(c(1, 5, 6))

supermarkets_sf[rowSums(is.na(supermarkets_sf))!=0,]

Simple feature collection with 0 features and 3 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] LIC_NAME POSTCODE LIC_NO   geometry
<0 rows> (or 0-length row.names)

supermarkets_sf <- st_make_valid(supermarkets_sf)

is.null(supermarkets_sf)

[1] FALSE

supermarkets_sf <- na.omit(supermarkets_sf)

write_rds(supermarkets_sf, "data/new/supermarkets_sf.rds")

Hexagon

# Hexagon for Map
hexagon <- st_read(dsn = "data/geospatial", layer = "hexagons")

Reading layer `hexagons' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-Project\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 3125 features and 6 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 21506.33 xmax: 50010.26 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM

hexagon_sf <- st_transform(hexagon, 3414)

write_rds(hexagon_sf, "data/model/hexagon_sf.rds")

Population

# Population Data 2022
popdata <- read_csv("data/aspatial/respopagesexfa2022.csv")

popdata2022 <- popdata %>%
  spread(AG, Pop) %>%
  mutate(YOUNG = `0_to_4`+`5_to_9`+`10_to_14`+
`15_to_19`+`20_to_24`) %>%
mutate(`ECONOMY ACTIVE` = rowSums(.[9:13])+
rowSums(.[15:17]))%>%
mutate(`AGED`=rowSums(.[18:22])) %>%
mutate(`TOTAL`=rowSums(.[5:22])) %>%  
mutate(`DEPENDENCY` = (`YOUNG` + `AGED`)
/`ECONOMY ACTIVE`) %>%
mutate_at(.vars = vars(PA, SZ), toupper) %>%
dplyr::select(`PA`, `SZ`, `YOUNG`, `ECONOMY ACTIVE`, `AGED`, 
       `TOTAL`, `DEPENDENCY`) %>%
  filter(`ECONOMY ACTIVE` > 0)

popdata2022 <- popdata2022 %>%
  mutate_at(.vars = vars(PA, SZ), 
          .funs = funs(toupper)) %>%
  filter(`ECONOMY ACTIVE` > 0)

write_rds(popdata2022, "data/model/popdata2022.rds")

mpsz_popdata2022 <- left_join(mpsz, popdata2022, by = c("SUBZONE_N" = "SZ"))

write_rds(mpsz_popdata2022, "data/model/mpsz_popdata2022.rds")

summary(mpsz_popdata2022$DEPENDENCY)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.6786  0.8645  0.9583  1.0000 17.0000      99 

EDA - Visualisation

MRT & MPSZ

mrt_mpsz <- st_join(mrt_sf, mpsz)

mrt_mpsz <- mrt_mpsz %>%
  select(c(1,7))

mrt_mpsz <- na.omit(mrt_mpsz)

# MRT vs MPSZ
tmap_mode('view')
tmap_options(check.and.fix = TRUE) +
tm_shape(mpsz) +
  tm_polygons() +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07) +
  tm_view(set.zoom.limits = c(11,14))

write_rds(mrt_mpsz, "data/model/mrt_mpsz.rds")

MRT & Population Density

# MRT vs Population Density
tmap_mode('view')
tm_shape(mpsz_popdata2022) +
  tm_fill("DEPENDENCY",
          breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) +
  tm_borders(lwd = 0.1,  alpha = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14))

MRT & Tourist Attraction

# MRT vs Tourism
tmap_mode('view')
tmap_options(check.and.fix = TRUE)
tm_shape(tourism_sf) +
  tm_dots(size = 0.1, col = 'yellow') +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14))

MRT & Shopping Malls

# MRT vs Shopping Malls
tmap_mode('view')
tmap_options(check.and.fix = TRUE)
tm_shape(shopping_sf) +
  tm_dots(size = 0.1, col = 'yellow') +
tm_shape(mrt_sf) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14))

Accessibility

Import Shapefiles

# Hexagon for Map
hexagon <- readRDS("data/model/hexagon_sf.rds")

# Tourism
tourism <- readRDS("data/model/tourism_sf.rds")

shopping <- readRDS("data/model/shopping_sf.rds")

# Import MRT Stations
mrt_mpsz <- readRDS("data/model/mrt_mpsz.rds")

#Import MRT Rail
mrt_rail <- readRDS("data/model/rail_sf.rds")

Calculate Distance Matrix

hexagon <- hexagon %>%
  dplyr::select(fid) %>%
  mutate(demand = 100)

mrt_mpsz <- mrt_mpsz %>%
  mutate(demand = 100)

shopping <- shopping %>%
  mutate(capacity = 100)

tourism <- tourism %>%
  dplyr::select(PAGETITLE) %>%
  mutate(capacity = 100)

Hexagon x Tourism

tourism_coord <- st_coordinates(tourism)
hexagon_coord <- st_centroid(hexagon) %>%
  st_coordinates()

hex_tour <- SpatialAcc::distance(hexagon_coord,
                                  tourism_coord,
                                  type = "euclidean")

hex_tour_km <- as.matrix(hex_tour/1000)

Hansen

acc_Hansen <- data.frame(ac(hexagon$demand,
                            tourism$capacity,
                            hex_tour_km,
                            #d0 = 50,
                            power = 2,
                            family = "Hansen"))

colnames(acc_Hansen) <- "accHansen"
acc_Hansen <- as_tibble(acc_Hansen)
hex_tour_Hansen <- bind_cols(hexagon, acc_Hansen)

write_rds(hex_tour_Hansen, "data/model/hex_tour_Hansen.rds")

tmap_mode("view")
tm_shape(hex_tour_Hansen,
         bbox = st_bbox(hex_tour_Hansen)) + 
  tm_fill(col = "accHansen",
          n = 10,
          style = "quantile",
          border.col = "black",
          border.lwd = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(tourism) +
  tm_symbols(size = 0.1) +
  tm_layout(main.title = "Accessibility to Tourism: Hansen Method",
            main.title.position = "center",
            main.title.size = 2,
            legend.outside = FALSE,
            legend.height = 0.45, 
            legend.width = 3.0,
            legend.format = list(digits = 6),
            legend.position = c("right", "top"),
            frame = TRUE) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar(width = 0.15) +
  tm_grid(lwd = 0.1, alpha = 0.5)

KD2SFCA

acc_KD2SFCA <- data.frame(ac(hexagon$demand,
                            tourism$capacity,
                            hex_tour_km,
                            d0 = 50,
                            power = 2,
                            family = "KD2SFCA"))

colnames(acc_KD2SFCA) <- "accKD2SFCA"
acc_KD2SFCA <- as_tibble(acc_KD2SFCA)
hex_tour_KD2SFCA <- bind_cols(hexagon, acc_KD2SFCA)

write_rds(hex_tour_KD2SFCA, "data/model/hex_tour_KD2SFCA.rds")

tmap_mode("view")
tm_shape(hex_tour_KD2SFCA,
         bbox = st_bbox(hex_tour_KD2SFCA)) + 
  tm_fill(col = "accKD2SFCA",
          n = 10,
          style = "quantile",
          border.col = "black",
          border.lwd = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(tourism) +
  tm_symbols(size = 0.1) +
  tm_layout(main.title = "Accessibility to Tourism: Hansen Method",
            main.title.position = "center",
            main.title.size = 2,
            legend.outside = FALSE,
            legend.height = 0.45, 
            legend.width = 3.0,
            legend.format = list(digits = 6),
            legend.position = c("right", "top"),
            frame = TRUE) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar(width = 0.15) +
  tm_grid(lwd = 0.1, alpha = 0.5)

SAM

acc_SAM <- data.frame(ac(hexagon$demand,
                            tourism$capacity,
                            hex_tour_km,
                            d0 = 50,
                            power = 2,
                            family = "SAM"))

colnames(acc_SAM) <- "accSAM"
acc_SAM <- as_tibble(acc_SAM)
hex_tour_SAM <- bind_cols(hexagon, acc_SAM)

write_rds(hex_tour_SAM, "data/model/hex_tour_SAM.rds")

tmap_mode("view")
tm_shape(hex_tour_SAM,
         bbox = st_bbox(hex_tour_SAM)) + 
  tm_fill(col = "accSAM",
          n = 10,
          style = "quantile",
          border.col = "black",
          border.lwd = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(tourism) +
  tm_symbols(size = 0.1) +
  tm_layout(main.title = "Accessibility to Tourism: Hansen Method",
            main.title.position = "center",
            main.title.size = 2,
            legend.outside = FALSE,
            legend.height = 0.45, 
            legend.width = 3.0,
            legend.format = list(digits = 6),
            legend.position = c("right", "top"),
            frame = TRUE) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar(width = 0.15) +
  tm_grid(lwd = 0.1, alpha = 0.5)

MRT x Tourism

tourism_coord <- st_coordinates(tourism)
mrt_coord <- st_centroid(mrt_mpsz) %>%
  st_coordinates()

mrt_tour <- SpatialAcc::distance(mrt_coord,
                                  tourism_coord,
                                  type = "euclidean")

mrt_tour_km <- as.matrix(mrt_tour/1000)

Hansen

acc_Hansen <- data.frame(ac(mrt_mpsz$demand,
                            tourism$capacity,
                            mrt_tour_km,
                            #d0 = 50,
                            power = 2,
                            family = "Hansen"))

colnames(acc_Hansen) <- "accHansen"
acc_Hansen <- as_tibble(acc_Hansen)
mrt_tour_Hansen <- bind_cols(mrt_mpsz, acc_Hansen)

write_rds(mrt_tour_Hansen, "data/model/mrt_tour_Hansen.rds")

# MRT & Tourism
tmap_mode('view')
tm_shape(tourism) +
  tm_symbols(size = 0.5) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.1, size=0.1)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(mrt_tour_Hansen) +
  tm_bubbles(col = "accHansen",
             n = 5,
             style = "quantile",
             size = 0.2,
             border.col = "black",
             border.lwd = 1)

Boxplot

mrt_tour_boxplot <- mrt_tour_Hansen

write_rds(mrt_tour_boxplot, "data/model/mrt_tour_boxplot.rds")

ggplot(data=mrt_tour_boxplot, 
       aes(y = log(accHansen), 
           x= REGION_N)) +
  geom_boxplot() +
  stat_summary(fun.y = "mean", 
                   geom = "point", 
                   colour = "red", 
                   size = 2)